Regression Analysis
This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models.
Overview
This course is designed to teach sometimes tricky statistical concepts in an easy-to-understand business and real-world context. It relies on the innovative text “Introductory Statistics and Analytics: A Resampling Perspective” and intentionally references the growing field of Data Science.
The course is divided into two four-week sections, Part 1 – Probability and Study Design, and Part 2 – Inference and Association.
The course is approved for academic credit recommendation (3 credits) by the American Council on Education.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
After completing this course you should be able to calculate both simple and multiple regression models. You will learn how to assess the model’s “fit”, test model assumptions, and transform predictor and response variables to improve outcomes. You will also learn to identify critical aspects of the data that can influence results of your model and how to exercise caution with respect to extrapolation from regression results.
- Calculate a simple linear regression model
- Assess the model with standard error, R-squared, and slope
- Review and check model assumptions
- Extend the model to multiple linear regression
- Assess parameter estimates globally, in subsets, and individually
- Test model assumptions
- Deal with qualitative predictors
- Transform predictors and response variables to improve model fit
- Handle interactions among predictors
- Identify influential points
- Deal with autocorrelation, multicollinearity, and missing data
- Exercise appropriate caution with respect to extrapolation
Who Should Take This Course
Scientists, business analysts, engineers and researchers who need to model relationships in data in which a single response variable depends on multiple predictor variables. If you were introduced to regression in an introductory statistics course and now find you need a more solid grounding in the subject, this course is for you. If you are planning to learn additional topics in statistics, a good knowledge of regression is often essential.
Our Instructors
Dr. Iain Pardoe
Dr. Iain Pardoe teaches online and writes courses for Thompson Rivers University Open Learning. He also does statistical consulting and was formerly an Associate Professor of Decision Sciences at the University of Oregon Lundquist College of Business. His research specialty is in the area of multivariate modeling. He has numerous journal publications (including a noted paper in the Journal of the Royal Statistical Society on predicting Academy Award winners).
Course Syllabus
Week 1
Foundations and Simple Linear Regression
- Brief review of univariate statistical ideas: confidence intervals
hypothesis testing
prediction - confidence intervals
- hypothesis testing
- prediction
- Simple linear regression model and least squares estimation
- Model evaluation: regression standard error
R-squared
testing the slope - regression standard error
- R-squared
- testing the slope
- Checking model assumptions
- Estimation and prediction
Week 2
Multiple Linear Regression
- Multiple linear regression model and least squares estimation
- Model evaluation: regression standard error
R-squared
testing the regression parameters globally
testing the regression parameters in subsets
testing the regression parameters individually - regression standard error
- R-squared
- testing the regression parameters globally
- testing the regression parameters in subsets
- testing the regression parameters individually
- Checking model assumptions
- Estimation and prediction
Week 3
Model Building I
- Predictor transformations
- Response transformations
- Predictor interactions
- Qualitative predictors and the use of indicator variables
Week 4
Model Building II
- Influential points (outliers and leverage)
- Autocorrelation
- Multicollinearity
- Excluding important predictors
- Overfitting
- Extrapolation
- Missing data
- Model building guidelines
- Model interpretation using graphics
Class Dates
2024
Instructors: Dr. Iain Pardoe
Instructors: Dr. Iain Pardoe
Instructors: Dr. Iain Pardoe
2025
Instructors: Dr. Iain Pardoe
Instructors: Dr. Iain Pardoe
Instructors: Dr. Iain Pardoe
Prerequisites
Introductory Statistics for Credit
- Skill: Intermediate
- Credit Options: ACE, CAP, CEU
The Statistics.com courses have helped me a lot, pushing me to the limit and making me learn much more than I expected I could. The knowledge I gained I could immediately leverage in my job … then eventually led to landing a job in my dream company – Amazon.
Karolis Urbonas
This program has been a life and work game changer for me. Within 2 weeks of taking this class, I was able to produce far more than I ever had before.
Susan Kamp
The material covered in the Analytics for Data Science Certificate will be indispensable in my work. I can’t wait to take other courses. Great work!
Stephen McAllister
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
Amir Aminimanizani
This is the best online course I have ever taken. Very well prepared. Covers a lot of real-life problems. Good job, thank you very much!
Elena Rose
The more courses I take at Statistics.com, the more appreciation I have for the smart approach, quality of instructors, assistants, admin and program. Well done!
Leonardo Nagata
This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
Richard Jackson
Frequently Asked Questions
-
What is your satisfaction guarantee and how does it work?
-
Can I transfer or withdraw from a course?
-
Who are the instructors at Statistics.com?
Visit our knowledge base and learn more.
Additional Information
Time Requirements
About 15 hours per week, at times of your choosing.
Homework
Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.
Course Text
The required text for this course is Applied Regression Modeling, Third Edition by Iain Pardoe.
Software
You will need software that is capable of doing regression analysis, which all statistical software does. If you are undecided about which package to choose, consider the following:
1. If you are likely to take additional statistical modeling courses and intend to apply these methods to your research, you should choose a standard package with power and flexibility (R, SAS, JMP, SPSS, Minitab, Stata).
2. If your plans include applications of data science and data analytics in business, you should probably choose R (if your company already uses SAS or SPSS, that’s also fine).
3. If you want to work as a manager or analyst in business, but not as a data scientist, you could use an Excel add-in like XLStat or XLMiner (the latter does not cover all the procedures in the course).
4. If you have no immediate plans for further coursework and a short learning curve is your main consideration, consider Statcrunch, JMP or Minitab.
The instructor is most familiar with R and Minitab. There will be some supplementary materials in the course to provide assistance with R, SPSS, Minitab, SAS, JMP, EViews, Stata, and Statistica. Our teaching assistants can offer some help with R, Minitab, SAS, JMP, Stata, Excel, and StatCrunch.
Course Fee & Information
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
Chrome
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
Firefox
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions
Safari
- Navidys (for colorblindness, dyslexia, and reading difficulties)
- HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)